SIASM: Sparsity-based image alignment and stitching method for robust image mosaicking

Abstract

Image alignment and stitching continue to be the topics of great interest. Image mosaicking is a key application that involves both alignment and stitching of multiple images. Despite significant previous effort, existing methods have limited robustness in dealing with occlusions and local object motion in different captures. To address this issue, we investigate the potential of applying sparsity-based methods to the task of image alignment and stitching. We formulate the alignment problem as a low-rank and sparse matrix decomposition problem under incomplete observations (multiple parts of a scene), and the stitching problem as a multiple labeling problem which utilizes the sparse components. Additionally we develop efficient algorithms for solving them. Unlike typical pairwise alignment manners in classical image alignment algorithms, our algorithm is capable of simultaneously aligning multiple images, making full use of inter-frame relationships among all images. Experimental results demonstrate that the proposed algorithm is capable of generating artifact-free stitched image mosaics that are robust against occlusions and object motion.